activity
20172022
most citedPWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction

92 citations · 123 across the 7 of their papers we have counts for

collaborators

7 papers

eess.IV2022★ 1 cited

Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging

Il Yong Chun, Dongwon Park, Xuehang Zheng +2

Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies. Yet, studying and understanding self-su…

eess.IV2020★ 2 cited

Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction

Xikai Yang, Xuehang Zheng, Yong Long +1

Signal models based on sparse representation have received considerable attention in recent years. Compared to synthesis dictionary learning, sparsifying transform learning involve…

eess.IV2019

BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization

Il Yong Chun, Xuehang Zheng, Yong Long +1

Obtaining accurate and reliable images from low-dose computed tomography (CT) is challenging. Regression convolutional neural network (CNN) models that are learned from training da…

eess.IV2019

Two-layer Residual Sparsifying Transform Learning for Image Reconstruction

Xuehang Zheng, Saiprasad Ravishankar, Yong Long +2

Signal models based on sparsity, low-rank and other properties have been exploited for image reconstruction from limited and corrupted data in medical imaging and other computation…

stat.ML2017★ 7 cited

Sparse-View X-Ray CT Reconstruction Using Prior with Learned Transform

Xuehang Zheng, Il Yong Chun, Zhipeng Li +2

A major challenge in X-ray computed tomography (CT) is reducing radiation dose while maintaining high quality of reconstructed images. To reduce the radiation dose, one can reduce…

stat.ML2017★ 21 cited

Low Dose CT Image Reconstruction With Learned Sparsifying Transform

Xuehang Zheng, Zening Lu, Saiprasad Ravishankar +2

A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images. We propose a new…

stat.ML2017★ 92 cited

PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction

Xuehang Zheng, Saiprasad Ravishankar, Yong Long +1

The development of computed tomography (CT) image reconstruction methods that significantly reduce patient radiation exposure while maintaining high image quality is an important a…